Abstract
In the production process of steel strips, the detection of surface defects is very important. However, traditional methods of defect detection bring problems of low detection accuracy and dependence on subjective judgment. In this study, the surface defects of steel strips are detected by a classic convolutional neural network method that is improved by the use of a transfer learning model. This model has the advantages of shorter training time, faster convergence, and more accurate weight parameters. The transfer learning model obtained through experiments secures better results in defect detection than the classic convolutional neural network method, as its accuracy of training and testing has reached about 98%. Finally, a model based on a full convolutional neural network (FCN) is proposed for segmenting the defective areas of steel strips.
Highlights
As an important steel product, the hot-rolled strip is widely used in manufacturing industry [1,2,3]
As well as improving the production conditions and production process and reducing the possibility of surface defects, it is necessary to do a good job of surface defect detection
In order to use a surface detection method based on a convolutional neural network, a complete and effective data set is required
Summary
As an important steel product, the hot-rolled strip is widely used in manufacturing industry [1,2,3]. The traditional methods of manual detection and classical pattern recognition described above are highly dependent on the experience parameter-setting of operators or algorithms These methods are often only suitable for the inspection of strip steel under specific conditions. Due to the excellent performance of deeplearning technology in many visual tasks [9, 10], a method based on deep learning is proposed for the automatic detection of defects on steel surfaces, replacing manual inspection and traditional pattern-recognition methods. The effectiveness of the algorithm has been verified by experiments He et al [12] proposed a hierarchical learning framework based on convolutional neural networks to classify the defects of hot-rolled steel and introduced a multiscale receiving field (MSRF) to be used together with the pretraining model concept-v4 to extract multiscale features. The training time of the transfer learning model [16] is shorter, its convergence speed is faster, the weight parameters are more accurate, and the accuracy of its detection of the surface defects of strip steel is higher
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